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DeepLearningforNaturalLanguageProcessing.CreatingNeuralNetworkswithPy
Thisbookattemptstosimplifyandpresenttheconceptsofdeeplearninginaverycomprehensivemanner,withsuitable,full-fledgedexamplesofneuralnetworkarchitectures,suchasRecurrentNeuralNetworks(RNNs)andSequencetoSequence(seq2seq),forNaturalLanguageProcessing(NLP)tasks.Thebooktriestobridgethegapbetweenthetheoreticalandtheapplicable.Itproceedsfromthetheoreticaltothepracticalinaprogressivemanner,firstbypresentingthefundamentals,followedbytheunderlyingmathematics,and,finally,theimplementationofrelevantexamples.ThefirstthreechapterscoverthebasicsofNLP,startingwiththemostfrequentlyusedPythonlibraries,wordvectorrepresentation,andthenadvancedalgorithmslikeneuralnetworksfortextualdata.Thelasttwochaptersfocusentirelyonimplementation,dealingwithsophisticatedarchitectureslikeRNN,LongShort-TermMemory(LSTM)Networks,Seq2seq,etc.,usingthewidelyusedPythontoolsTensorFlowandKeras.Wehavetriedourbesttofollowaprogressiveapproach,combiningalltheknowledgegatheredtomoveontobuildingaquestionand-answersystem.Thebookoffersagoodstartingpointforpeoplewhowanttogetstartedindeeplearning,withafocusonNLP.AllthecodepresentedinthebookisavailableonGitHub,intheformofIPythonnotebooksandscripts,whichallowsreaderstotryouttheseexamplesandextendthemininteresting,personalways.
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